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نتیجه جستجو - صنعت 4:0

تعداد مقالات یافته شده: 10
ردیف عنوان نوع
1 اجرای فناوری صنعت 4:0 در SMEs - یک مرور در منطقه مرزی دانمارک و آلمان
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 22
صنعت 4.0 ، که به عنوان چهارمین فناوری تحول برای سیستم های فیزیکی دیجیتال در تولید شناخته شده ، تأثیر مخربی بر صنایع ایجاد می کند. شرکت های تولیدی ، به ویژه شرکت های کوچک و متوسط ، با چالش های مختلفی روبرو هستند و برای ادامه رقابت باید دائماً نوآوری داشته باشند. یکی از راه های نوآوری ، اجرای فناوری های جدید در فرایندهای شرکت است. در این مطالعه ، ما چگونگی ارتباط فناوری ، شرکت و صنعت در ارتباط با اجرای صنعت 4.0 در SME ها را بررسی می کنیم. داده ها را از طریق نظرسنجی با تمرکز بر صنعت 4.0 در SME جمع آوری کردیم. نتایج نشان داد که دانش و مزایای مورد انتظار فناوری، محرکی برای اجرای فناوری های صنعت 4.0 هستند. همچنین نتایج نشان داد که به احتمال زیاد، شرکتهای دارای سطح بالایی از اتوماسیون فرآیند و تنوع بالای محصول ، فناوریهای صنعت 4.0 را اجرای می کنند. این مطالعه درک بهتری از وضعیت ، چالش ها و برنامه های اجرای صنعت 4.0 در SME ها ایجاد می کند ، که از توسعه ابزارها و سیستم های تولید مناسب SME و درک مدیران صنعت و سیاست گذاران از فناوری های صنعت 4.0 پشتیبانی می کند.
صنعت 4.0 | SME ها | پیاده سازی فناوری | مرور-بررسی
مقاله ترجمه شده
2 Text mining of industry 4:0 job advertisements
استخراج متن آگهی های شغلی صنعت 4:0-2020
Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.
Keywords: Human resource management | Text mining | Job profiles | Big data analytics | Industry 4.0 | Education | Smart factory
مقاله انگلیسی
3 Big data and stream processing platforms for Industry 4:0 requirements mapping for a predictive maintenance use case
چهارچوب داده های بزرگ و پردازش جریان برای نگاشت الزامات صنعت 4:0 برای یک مورد استفاده نگهداری پیشگویانه-2020
Industry 4.0 is considered to be the fourth industrial revolution introducing a new paradigm of digital, autonomous, and decentralized control for manufacturing systems. Two key objectives for Industry 4.0 applications are to guarantee maximum uptime throughout the production chain and to increase productivity while reducing production cost. As the data-driven economy evolves, enterprises have started to utilize big data techniques to achieve these objectives. Big data and IoT technologies are playing a pivotal role in building data-oriented applications such as predictive maintenance. In this paper, we use a systematic methodology to review the strengths and weaknesses of existing opensource technologies for big data and stream processing to establish their usage for Industry 4.0 use cases. We identified a set of requirements for the two selected use cases of predictive maintenance in the areas of rail transportation and wind energy. We conducted a breadth-first mapping of predictive maintenance use-case requirements to the capabilities of big data streaming technologies focusing on open-source tools. Based on our research, we propose some optimal combinations of open-source big data technologies for our selected use cases.
Keywords: Industry 4.0 | Big Data | Stream processing | Predictive maintenance | Railway | Wind turbines
مقاله انگلیسی
4 The role of absorptive capacity and innovation strategy in the design of industry 4:0 business Models-A comparison between SMEs and large enterprises
نقش ظرفیت جذب و استراتژی نوآوری در طراحی مدل های تجاری صنعت 4:0 -مقایسه بین SME ها و شرکت های بزرگ-2020
Technological innovations often lead to redesigns in the business models of established companies, requiring them to incorporate new external knowledge into internal activities. Against this background, this study integrates the concepts of business model design, absorptive capacity, and innovation strategy into a novel research model, which analyzes the redesign of established business models in response to the emergence of Industry 4.0. Industry 4.0, also known as the Industrial Internet of Things, constitutes a contemporary research context that is highly relevant for corporate practice but scarcely regarded in management literature until now. The article contains an analysis of data from 221 German industrial enterprises, conducted through structural equation modeling, with separate data for small and medium- sized enterprises (SMEs) and large enterprises. First, the results indicate that the acquisition, assimila- tion, transformation, and exploitation of knowledge from the environment enable companies to engage in both exploratory and exploitative innovation strategies. Furthermore, the paper includes an evaluation of the role of exploratory and exploitative innovation strategies that reflects in efficiency-centered and novelty-centered business model designs. The distinct characteristics differentiating SMEs from large enterprises are also explained. The implications of absorptive capacity on innovation strategies, which influence the redesign of extant business models, are discussed from a research and managerial perspective.© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Business model design | Industry 4.0 | Industrial internet of things | Digital transformation | Absorptive capacity | Exploratory innovation strategy | Exploitative innovation strategy | Small and medium-sized enterprises (SMEs)
مقاله انگلیسی
5 Industry 4:0 based process data analytics platform: A waste-to-energy plant case study
پلت فرم تجزیه و تحلیل داده های مبتنی بر فرآیند صنعت 4:0: مطالعه موردی از گیاهان زباله به انرژی-2020
Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling data and software solutions driven digitalization in many fields notably in industrial automation and manufacturing systems. Among the several benefits offered by these technologies, is the infrastructure for harnessing big-data, machine learning (ML) and cloud computing software tools, for instance in designing advanced data analytics platforms. Although, this is an area of increased interest, the information concerning the implementation of data analytics in the context of Industry 4.0 is scarcely available in scientific literature. Therefore, this work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art IIoT platforms, ML algorithms and big-data software tools. The platform emphasizes the use of ML methods for process data analytics while leveraging big-data processing tools and taking advantage of the currently available industrial grade cloud computing platforms. The industrial applicability of the platform was demonstrated by the development of soft sensors for use in a waste-to-energy (WTE) plant. In the case study, the work studied datadriven soft sensors to predict syngas heating value and hot flue gas temperature. Among the studied data-driven methods, the neural network-based NARX model demonstrated better performance in the prediction of both syngas heating value and flue gas temperature. The modeling results showed that, in cases where process knowledge about the process phenomena at hand is limited, data-driven soft sensors are useful tools for predictive data analytics.
Keywords: Data analytics platform | Industrial internet of things platform | Machine learning | Waste-to-energy | Soft sensor
مقاله انگلیسی
6 Circular economy meets industry 4:0: Can big data drive industrial symbiosis?
تطابق اقتصاد مدور با صنعت 4:0: آیا داده های بزرگ می توانند درهم آمیختگی صنعتی ایجاد کنند؟-2018
Cross-industry networks of multiple supply chains have evolved in the circular economy model using approaches such as industrial and urban symbiosis. However, the implementation of such sustainable industrial networks with matrix-like structures is not straightforward. Despite the clear benefits of big data-driven industrial sym biosis, corporates have noted that social, environmental and economic perspectives are also highly appreciated in the cross-industry networks. Moreover, gaps remain in operational data-driven and recycle, reduce and reuse optimization solutions, which may be the key components of industrial symbiosis practices.
Keywords: Circular economy ، Data-driven analysis method ، Big data-driven industrial symbiosis ، Industrial 4.0
مقاله انگلیسی
7 Digital Twin and Big Data Towards Smart Manufacturing and Industry 4:0: 360 Degree Comparison
دوقلوی دیجیتال و داده های بزرگ به سوی ساخت هوشمند و صنعت 4:0: مقایسه 360 درجه ای-2018
With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.
INDEX TERMS : Big data, digital twin, smart manufacturing, comprehensive comparison, convergence
مقاله انگلیسی
8 قابلیت ردیابی در زنجیره تامین غذایی: مرور بر ادبیات از دیدگاه فناوری
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 11
صنایع غذایی مشتری محور به طور روزافزون نیاز به زمان ردیابی سریع تر دارد تا به زنجیره های تامین همکاری آمیز و پویا رسیدگی کند. سیستم های ردیابی به حداقل رساندن تولید و توزیع محصولات کیفیت ضعیف یا غیرایمن کمک می کند. لذا قابلیت ردیابی به عنوان ابزاری به کار گرفته می شود تا از ایمن و کیفیت غذا اطمینان حاصل گردد، و نیز اینکه اعتماد مشتری جلب گردد. فناوری های جدید مورد استفاده قرار می گیرند و پیشنهاد جدید در این زمینه آزموده می شوند. صنعت 4.0 شامل انواع فناوری ها است که توسعه محیط تولیدی خودکار و دیجیتال را مقدور می سازد. این فناوری های جدید به مفهوم تغییر اساسی در نحوه عملکرد شرکت ها می باشند. این فناوری ها تاثیر زیادی بر جوانب مختلف شرکت داشته و به طور طبیعی فرایندهای ردیابی در این موج جدید تغییرات شامل می شوند. دو واژه «پیگردی» و «پیگیری» در مسئله ردیابی بحث می شوند. پیگیری به عنوان فرایند معطوف به گذشته تعریف می گردد که در آن سابقه زنجیره تامین شناسایی می گردد و پیگردی فرایند مستقیم است که در آن کاربران نهایی و شرکا تجاری با تعیین محل در زنجیره تامین شناسایی می شوند، هر دو واژه در زنجیره تامین به کار می روند. در این اثر مرور بر ادبیات در اجرا صنعت 4.0 مبنی بر ردیابی در زنجیره تامین غذایی مطرح می شود. نکته اینجا است که علاقه به این زمینه مطالعاتی بیشتر شده است. به علاوه، تکامل زمانی فناوری های به کار رفته در ردیابی زنجیره تامین به روز فزاینده پیچیده می گردد که به خاطر شامل سازی پیشنهادات جدید است. سرانجام اینکه تحلیل فناوری های به کار رفته در زمینه های مختلف بخش غذایی مطرح می گردد و لذا پیشنهادات میوه، سبزیجات، گوشت یا ماهی تحلیل شده و فناوری های صنعت 4.0 در هر زمینه تعیین می شوند. این مقاله بخش هایی را نشان می دهد که هنوز پیشنهادات جدید به بررسی آنها نپرداخته اند و مسئله ردیابی نیز در صنعت 4.0 هنوز مطالعه نشده است. لذا، این مقاله امکان تعیین شکاف های تحقیقاتی در این زمینه را مقدور می سازد.
واژگان کلیدی: زنجیره تامین غذایی | قابلیت پاسخگویی | صنعت 4.0 | اینترنت اشیا
مقاله ترجمه شده
9 Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4:0
استنتاج بیزی برای کاوش تولید نیمه هادی داده های بزرگ برای افزایش عملکرد و تولید هوشمند برای تقویت صنعت 4:0-2018
Big data analytics have been employed to extract useful information and derive effective manufac turing intelligence for yield management in semiconductor manufacturing that is one of the most complex manufacturing processes due to tightly constrained production processes, reentrant process flows, sophisticated equipment, volatile demands, and complicated product mix. Indeed, the increasing adoption of multimode sensors, intelligent equipment, and robotics have enabled the Internet of Things (IOT) and big data analytics for semiconductor manufacturing. Although the processing tool, chamber set, and recipe are selected according to product design and previous experiences, domain knowledge has become less efficient for defect diagnosis and fault detection. To fill the gaps, this study aims to develop a framework based on Bayesian inference and Gibbs sampling to investigate the intricate semiconductor manufacturing data for fault detection to empower intelligent manufacturing. In addition, Cohen’s kappa coefficient was used to eliminate the influence of extraneous variables. The proposed approach was val idated through an empirical study and simulation. The results have shown the practical viability of the proposed approach.
Keywords: Bayesian approach ، Semiconductor manufacturing ، Multi-collinearity ، Yield enhancement ، Big data analytics ، Smart production
مقاله انگلیسی
10 Service innovation and smart analytics for Industry 4:0 and big data environment
نوآوری خدمات و تجزیه و تحلیل هوشمند برای صنعت 4:0 و محیط داده های بزرگ-2014
Today, in an Industry 4.0 factory, machines are connected as a collaborative community. Such evolution requires the utilizationof advanceprediction tools, so that data can be systematically processed into information to explain uncertainties, and thereby make more“informed” decisions. Cyber-Physical System-based manufacturing and service innovations are two inevitable trends and challenges for manufacturing industries. This paper addresses the trends of manufacturing service transformation in big data environment, as well as the readiness of smart predictive informatics tools to manage big data, thereby achieving transparency and productivity. Keywords: Manufacturing servitization; predictive maintenance; industrial big data
مقاله انگلیسی
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